System for interference cancellation

ABSTRACT

A spatial processor that exploits signals that arrive via multiple outputs of a communication channel to provide soft decision values useful to a trellis or Viterbi decoder. A spatial processor may take into account a statistical characterization of interference as received via the multiple channel outputs. Spatial processor operation may also be optimized to operate in conjunction with orthogonal frequency division multiplexing (OFDM).

BACKGROUND OF THE INVENTION

The present invention relates to digital communication systems and moreparticularly to systems and methods for exploiting multiple antennas orother channel outputs to exploit spatial diversity and ameliorate theeffects of interference.

It is known to use adaptive spatial processing to exploit multipleantenna arrays to increase the communication quality of wirelesssystems. A weighting among antennas is chosen based on content of thesignals received via multiple antenna elements. The spatial processorselects a weighting that optimizes reception of a desired signal.

In some systems, the spatial processor estimates a weighting based inpart on statistical characterization of an interference source. Thespatial processor selects the weighting to maximize the signal tointerference plus noise ratio (SINR). Examples of such systems aredescribed in PCT Pub. No. 98/18271, the contents of which areincorporated herein by reference.

In these systems, the weighting becomes a basis for a maximum likelihoodsolution for estimating transmitted symbols. The output of the spatialprocessor is a so-called “hard decision” of the most likely transmittedsymbol. It would be desirable, however, to combine trellis codes withthe use of this type of spatial processing at the receiver becausetrellis codes provide excellent bit error rate performance with minimaladdition of redundant information. A trellis decoder or Viterbi decoderwould then be used to remove the effects of the trellis coding. Ratherthan a “hard decision,” the trellis or Viterbi decoder requires as inputlikelihood or so-called “cost metric” values corresponding to differentvalues of each transmitted symbol. These cost metric values are alsoreferred to as “soft decision” values. It would be desirable to optimizespatial processing techniques to efficiently provide “soft decision”values as output rather than “hard decision” values.

OFDM (Orthogonal Frequency Division Multiplexing) is another highlyuseful communication technique. In OFDM, the available bandwidth isdivided into subchannels that are orthogonal to one another in thefrequency domain. A high data rate signal is effectively transmitted asa set of parallel low data rate signals, each one being carried over aseparate subchannel. OFDM addresses a problem known as multipath causedby differences in delay time among different paths taken from atransmitter to a receiver. The effect of multipath is intersymbolinterference created by energy associated with different symbols sharinga common arrival time. By creating multiple low data rate subchannels,OFDM lengthens the period occupied by a single symbol so that dispersiveeffects tend to be confined within a single symbol period, therebyreducing intersymbol interference. It would also be desirable tooptimize a soft decision output spatial processor to operate inconjunction with OFDM.

SUMMARY OF THE INVENTION

A spatial processor that exploits signals that arrive via multipleoutputs of a communication channel to provide soft decision valuesuseful to a trellis or Viterbi decoder is provided by virtue of thepresent invention. A spatial processor according to the presentinvention may take into account a statistical characterization ofinterference as received via the multiple channel outputs. Spatialprocessor operation may also be optimized to operate in conjunction withorthogonal frequency division multiplexing (OFDM) and therebyeffectively ameliorate the effects of frequency selective interference.

In accordance with a first aspect of the present invention, a method isprovided for receiving an OFDM signal via a plurality of outputs of achannel in the presence of noise and/or interference. The methodincludes: forming an estimate of a received OFDM frequency domain symbolbased on a statistical characterization of noise and/or interferencereceived via the plurality of channel outputs, obtaining a channelconfidence level for a frequency subchannel of the OFDM frequency domainsymbol, and obtaining cost metric values for various possible values ofthe received OFDM frequency domain symbol based on the estimate and thechannel confidence level.

A further understanding of the nature and advantages of the inventionsherein may be realized by reference to the remaining portions of thespecification and the attached drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1. depicts elements of a receiver system according to oneembodiment of the present invention.

FIG. 2 is a flowchart describing steps of estimating noise andinterference on a per channel output basis according to one embodimentof the present invention.

FIG. 3 is a flowchart describing steps of generating cost metric valuesaccording to one embodiment of the present invention.

FIG. 4 depicts the operation of constellation bit mapping according toone embodiment of the present invention.

DESCRIPTION OF SPECIFIC EMBODIMENTS

The present invention will now be described with reference to a wirelesscommunication system that employs multiple antennas at a receiver.According to the present invention, the communication system need not bewireless and the antennas referred to herein are merely examples ofconnections to outputs of a communication channel.

The present invention is also described in the context of the use ofOFDM (Orthogonal Frequency Division Multiplexing) for communication,although the present invention is not limited to OFDM. In OFDM, theavailable bandwidth is effectively divided into a plurality ofsubchannels that are orthogonal in the frequency domain. During a givensymbol period, the transmitter transmits a symbol in each subchannel. Tocreate the transmitted time domain signal corresponding to all of thesubchannels, an IFFT is applied to a series of frequency domain symbolsto be simultaneously transmitted, a “burst.” The resulting series oftime domain symbols is augmented with a cyclic prefix prior totransmission. The cyclic prefix addition process can be characterized bythe expression:

[z(1) . . . z(N)]^(T)[z(N−v+1) . . . z(N) z(1) . . . z(N)]^(T)

On the receive end, the cyclic prefix is removed from the received timedomain symbols. An FFT is then applied to recover the simultaneouslytransmitted frequency domain symbols. The cyclic prefix has length vwhere v is greater than or equal to a duration of the impulse responseof the overall channel and assures orthogonality of the frequency domainsubchannels.

There are other ways of simultaneously transmitting a burst of symbolsin orthogonal channels or substantially orthogonal channels including,e.g., use of the Hilbert transform, use of the wavelet transform, usinga batch of frequency upconverters in combination with a filter bank,etc. Wherever the term OFDM is used, it will be understood that thisterm includes all alternative methods of simultaneously communicating aburst of symbols in orthogonal or substantially orthogonal subchannels.The term frequency domain should be understood to refer to any domainthat is divided into such orthogonal or substantially orthogonalsubchannels.

FIG. 1. depicts elements of a receiver system 100 according to oneembodiment of the present invention. Receiver system 100 collectssignals from a plurality of antennas 102. In FIG. 1, two antennas areshown, although any number of antennas may be used. Many componentsdepicted in FIG. 1 are duplicated for each antenna.

Each antenna 102 is coupled to an RF/IF system 104 which performsinitial analog filtering and amplification prior to downconversion to anintermediate frequency (IF) where further filtering and signalconditioning may be performed. The signal is then converted to basebandfor input to an analog to digital converter 106. Alternatively, analogto digital conversion may occur at the IF. Further filtering andinterpolation occurs in an FIR filter block 108. The next stage is anFFT processor 110 that removes the cyclic prefix from N+v long timedomain symbol bursts and then applies the FFT to recover N frequencydomain symbols for each successive OFDM burst.

Spatial processing according to one embodiment of the present inventiondepends on estimating the response of the channel for every frequencydomain subchannel n among N frequency domain symbols. This is thefunction of a channel estimation processor 112. In one embodiment, atleast v of the N frequency domain symbols are training symbols havingknown transmitted values. The received values of these training symbolsare used to determine the channel response over the entire availablefrequency domain channel. Details of channel estimation techniques aredescribed in WO98/09385 and in co-filed U.S. patent application Ser. No.09/234,929, titled IMPROVED OFDM CHANNEL IDENTIFICATION, the contents ofwhich are herein incorporated by reference.

A symbol estimation block 114 forms an initial estimate of the value ofeach transmitted symbol based on the output of FFT processor 110 andchannel estimation processor 112. By subtracting the received value ofeach symbol from the transmitted value, a noise and interferenceestimation block 116 estimates the noise and interference at eachfrequency domain position independently for each antenna. Details of theoperation of symbol estimation block 114 and noise and interferenceestimation block 116 will be discussed in greater detail with referenceto FIG. 2.

A statistical characterization block 118 uses inputs from the variousnoise and interference estimation blocks to develop a statisticalcharacterization of the received noise and interference and itsdistribution among the antennas. This statistical characterization ispreferably developed independently for each frequency domain position n.It is useful to smooth over n to estimate noise and/or interference thatvaries over time. A cost metric value processor 120 weights inputs asreceived via the various antennas according to the statisticalcharacterization of noise and interference determined by noise andinterference estimation block 116. The output of cost metric valueprocessor 120 is one or more values which constitute a so-called softdecision value for each frequency domain symbol received. In oneembodiment, these values are given for each bit that makes up a symbol.Details of operation of statistical characterization block 118 and costmetric value processor block 120 are discussed in greater detail inreference to FIG. 3.

FIG. 2 is a flowchart describing steps of estimating noise andinterference on a per antenna basis according to one embodiment of thepresent invention. The steps of FIG. 2 are performed independently foreach frequency domain symbol for each antenna and are repeated for eachburst. At step 202, symbol estimation block 114 forms an initialestimate, {circumflex over (z)}_(i)(n), of the value of the symbolaccording to the following expression:${{\hat{z}}_{i}(n)} = \frac{{{\hat{h}}_{i}^{*}(n)}{x_{i}(n)}}{{{\hat{h}}_{i}^{*}(n)}{{\hat{h}}_{i}(n)}}$

where

n is a frequency domain index, i identifies a particular antenna,ĥ_(i)(n) is a channel response estimate at frequency domain index nbased on signal received via channel output i, x_(i)(n) represents areceived frequency domain symbol at frequency domain index n receivedvia antenna i. These quantities are complex scalars. The effect of step202 is to compensate for the known effect of the channel.

At step 204, noise and interference estimation block 116 forms anestimate of noise and interference according to the followingexpression:

ŵ _(i)(n)=x _(i)(n)−ĥ _(i)(n){circumflex over (z)} _(i)(n)

where z_(i)(n) is the nearest constellation value to the initial symbolestimate, {circumflex over (z)}_(i)(n). In the absence of interference,noise will dominate this expression.

FIG. 3 is a flowchart describing steps of generating cost metric valuesaccording to one embodiment of the present invention. At step 302,statistical characterization block 118 develops a statisticalcharacterization of the received noise and interference and itsdistribution among the antennas. In one embodiment, it obtains acovariance matrix R_(W)(n) that has M_(R)×M_(R) entries where M_(R) isthe number of receiver antennas. The ijth entry of this covariancematrix is determined as E[ŵ_(i)(n)ŵ*_(j)(n)] where i and j identifyindividual antennas.

The E expectation operator may be evaluated over a moving series ofbursts. Smoothing may be performed over either or both of time andfrequency depending on interference characteristics. Cost metric valueprocessor 120 bases its generation of cost metric values on thefollowing expression which gives a maximum likelihood hard decisionvalue for each frequency domain symbol:$\min\limits_{Z{(n)}}{{{\hat{H}}^{*}(n)}{R_{w}^{- 1}(n)}{\hat{H}(n)}{{{z(n)} - \frac{{{\hat{H}}^{*}(n)}{R_{w}^{- 1}(n)}{X(n)}}{{{\hat{H}}^{*}(n)}{R_{w}^{- 1}(n)}{\hat{H}(n)}}}}^{2}}$

where n is a frequency domain index, Ĥ is an M_(R) length vectorestimate of a response of the channel made up of the scalar valuesgenerated by channel estimation processors 112, R_(W) the M_(R)×M_(R)matrix described above indicating spatial covariance of noise and/orinterference, and X is an M_(R) length vector of received symbols madeup of scalar values generated by each FFT processor 110.

An alternative cost function that provides the same result is:

$\min\limits_{z{(n)}}{{R_{w}^{{- 1}/2}\left( {{X(n)} - {{\hat{H}(n)}{z(n)}}} \right.}_{2}^{2}}$

A cost function associated with this maximum likelihood expression maybe generated in two steps. At step 304, cost metric value processor 120generates a complex scalar estimate for the transmitted symbol at agiven frequency index by applying the expression:${\hat{z}(n)} = {\frac{{{\hat{H}}^{*}(n)}{R_{w}^{- 1}(n)}{X(n)}}{{{\hat{H}}^{*}(n)}{R_{w}^{- 1}(n)}{\hat{H}(n)}}.}$

Unlike the estimate obtained by symbol estimation block 114, thisestimate incorporates a weighting among all the antennas that optimizesthe signal to noise plus interference ratio of the combined estimate. Atstep 306, cost metric value processor block 120 determines a scalarchannel confidence level for each frequency domain subchannel byapplying the expression:

p(n)=Ĥ*(n)R _(w) ⁻¹(n)Ĥ(n).

Incorporating this confidence level into cost metric evaluations allowsthe decoding process that follows to effectively give greater weight tosymbols received via subchannels that have higher signal to noise plusinterference ratios.

At step 308, cost metric value processor block 120 determines the costmetric value for each frequency domain symbol for every burst. In oneembodiment, this cost metric value is given for each symbol z(n) as awhole by the expression:

c(n)=p(n)|z(n)−{circumflex over (z)}(n)|².

This type of cost metric value referred to in the art as “weightedEuclidean.” These symbol cost metrics are used by a complex trellisdecoder to remove the effects of convolutional or trellis coding. In analternative embodiment, constellation-bit-mapping (CBM) is employed sothat cost metric values are generated for each constituent bit of asymbol. This allows the use of more efficient and simpler bit-wiseViterbi decoding techniques. For CBM, the expression applied at step 304is resolved into I and Q (i.e., real and imaginary) components{circumflex over (z)}_(I)(n) and {circumflex over (z)}_(Q)(n). The CBMscheme will be explained with reference to a representative modulationscheme where the frequency domain symbols may take on one of 16 idealcomplex values when transmitted (16-QAM) and thus each symbol carriesfour bits of information. A single 16-QAM symbol may also be understoodto be a pair of symbols that each can take on one of four real values(4-PAM) and thus carries two bits of information. FIG. 4 depicts firstand second PAM constellations corresponding to the I and Q components ofa 16-QAM constellation along with representative positions for{circumflex over (z)}_(I)(n) and {circumflex over (z)}_(Q)(n) .

The cost metric values are determined separately for each I and Qcomponent and for each bit. Thus for each frequency domain symbol, thereare four associated cost values. The cost metric value is givenindependently for I and Q components by the expression:

c(n,m)=p(n)|d ₀(n,m)−d ₁(n,m)|²

where n is the frequency domain index, m identifies a particular bit inthe PAM symbol, and p(n) is the confidence level determined at step 306.d₀(n,m) is the distance from the relevant component of {circumflex over(z)} (n) to the nearest ideal PAM constellation symbol position havingthe value “0” at bit position m. Similarly, d₁(n,m) is the distance tothe nearest ideal constellation symbol position having the value “1” atbit position m. In FIG. 4, for the I-component for the MSB bit position,d₀(n,2) is the distance from {circumflex over (z)}_(I)(n) to the idealsymbol position having value 00. d₁(n,2) is the distance from{circumflex over (z)}_(I)(n) to the ideal symbol position having value11. For the LSB bit position, d₀(n,1) is the distance from {circumflexover (z)}_(I)(n) to the ideal symbol position having value 00. d₁(n,1)is the distance from {circumflex over (z)}_(I)(n) to the ideal symbolposition having value 01. These CBM calculations may be performed by alookup table having as inputs p(n) and either {circumflex over(z)}_(I)(n) or {circumflex over (z)}_(Q)(n) . The outputs are then themetric values for input to decoder 122.

The spatial processing techniques described above may be simplified byassuming that the interference is spatially uncorrelated among thevarious receiver antennas. Under this assumption,${R_{w}(n)} = \begin{bmatrix}{\sigma_{1}^{2}(n)} & 0 \\0 & {\sigma_{2}^{2}(n)}\end{bmatrix}$

for a two receiver antenna system.

Step 304 then becomes obtaining${\hat{z}(n)} = \frac{\sum\limits_{i = 1}^{M_{R}}{{{{\hat{h}}_{i}^{*}(n)}/{\sigma_{i}^{2}(n)}}{x_{i}(n)}}}{\sum\limits_{i = 1}^{M_{R}}{{{{\hat{h}}_{i}(n)}}^{2}/{\sigma_{i}^{2}(n)}}}$

and step 306 becomes obtaining${p(n)} = {\sum\limits_{i = 1}^{M_{R}}{{{\hat{h}(n)}}^{2}/{{\sigma_{i}^{2}(n)}.}}}$

The various noise and interference estimation blocks 116 need only fmdinterference energy for each tone and antenna. One technique is to findthe so-called “constellation variance” by applying the expression:

σ_(i) ²(n)=E{ĥ _(i)*(n)ĥ _(i)(n)|{circumflex over (z)} _(i)(n)−z(n)|²}where

where z(n) is the nearest ideal constellation value and {circumflex over(z)}_(i)(n) is the single antenna corrected symbol value:${{\hat{z}}_{i}(n)} = \frac{{{\hat{h}}_{i}^{*}(n)}{x_{i}(n)}}{{{\hat{h}}_{i}^{*}(n)}{{\hat{h}}_{i}(n)}}$

 found by symbol estimation block 114. This process of findingconstellation variances substitutes for step 302.

One may further simplify implementation of spatial processing accordingto the present invention by assuming that interference is not onlyspatially uncorrelated across antennas but also identically distributed.This means that

R _(w)(n)=σ²(n)I _(M) _(R)

where

σ² is the identical interference energy level at each antenna and whereI_(M) _(R) is the M_(R)×M_(R) identity matrix. Then step 304 becomesobtaining:${\hat{z}(n)} = {\frac{\sum\limits_{i = 1}^{M_{R}}{{{\hat{h}}_{i}^{*}(n)}{x_{i}(n)}}}{\sum\limits_{i = 1}^{M_{R}}{{{\hat{h}}_{i}(n)}}^{2}}.}$

At step 306, the channel confidence level is determined to be:${p(n)} = {\frac{1}{\sigma^{2}(n)}{\sum\limits_{i = 1}^{M_{R}}{{{{\hat{h}}_{i}(n)}}^{2}.}}}$

Step 302 becomes finding the interference energy at tone n, σ²(n), theaverage constellation variance obtained by:${\sigma^{2}(n)} = {E{\left\{ {\sum\limits_{i = 1}^{M_{R}}{{{{\hat{h}}_{i}(n)}}^{2}{{{\hat{z}(n)} - {z(n)}}}^{2}}} \right\}.}}$

Because the estimated channel value Ĥ(n) is also corrupted byinterference, it can be beneficial to average the channel confidencevalue p(n) by instead obtaining:${{p(n)} = \frac{1}{E\left\{ {{{\hat{z}(n)} - {z(n)}}}^{2} \right\}}},$

where, again, z(n) is the nearest ideal constellation value to{circumflex over (z)}(n) which is given by step 304 as calculatedaccording to the iid assumption.

It is sometimes useful to consider the inverse of the channel confidencelevel to be another measure of interference energy:${q(n)} = {\frac{1}{p(n)} = {E{\left\{ {{{\hat{z}(n)} - {z(n)}}}^{2} \right\}.}}}$

It is also often useful to smooth interference energy across frequencyor over successive bursts as part of the spatial processing. Thissmoothing may be applied to any of the quantitities: σ²(n),σ_(i)²(n),R_(w)(n), or q(n). For this discussion of smoothing, all of thesequantities will be referred to as r(n,k) at tone n and burst k.

One way to smooth is to use a square window across M frequency positionsby obtaining:${{\overset{\_}{r}\left( {n,k} \right)} = {\frac{1}{M}{\sum\limits_{m = {n - {M/2}}}^{n + {M/2}}{r\left( {m,k} \right)}}}},$

∀n. The information carried by the quantity r(n,k) can also becompressed to fewer than v values since interference tends to pollutecontiguous sets of tones. One may for example obtain interferenceaverages for each group of seven tones using the expression:${{\overset{\_}{r}\left( {n,k} \right)} = {\frac{1}{7}{\sum\limits_{m = {n - 3}}^{n + 3}{r\left( {m,k} \right)}}}},\quad {\forall{n \in {\left\{ {4,12,{20\quad \ldots}}\quad \right\}.}}}$

Also, one can smooth over successive bursts. For example, one may use anexponential window to incorporate values of previous bursts by applyingthe expression:

{overscore (r)}(n,k)=β{overscore (r)}(n,k−1)+(1−β)r(n,k).

If r(n,k)=R_(w)(n,k), exponential averaging from burst to burst may beapplied as:

R_(w)(n,k)=βR_(w)(n,k−1)+(1−β)w(n,k)w*(n,k). A representative value of βis, e.g., {fraction (15/16)}.

It is understood that the examples and embodiments described herein arefor illustrative purposes only and that various modifications or changesin light thereof will be suggested to persons skilled in the art and areto be included within the spirit and purview of this application andscope of the appended claims and their full scope of equivalents. Forexample, all formulas given above are merely representative ofprocedures that may be used. Functionality may be added or deleted fromFIG. 1 and operations may be interchanged among functional blocks. Forthe flowchart of FIGS. 2-3, steps may be added or deleted within thescope of the present invention. All publications, patents, and patentapplications cited herein are hereby incorporated by reference.

What is claimed is:
 1. In a digital communication system, a method forreceiving an OFDM signal via a plurality of outputs of a channel in thepresence of noise and/or interference, the method comprising: forming anestimate of a received OFDM frequency domain symbol based on astatistical characterization of noise and/or interference received viasaid plurality of channel outputs; obtaining a channel confidence levelfor a frequency subchannel of said OFDM frequency domain symbol; andobtaining a cost metric value of said received OFDM frequency domainsymbol based on said estimate and said channel confidence level.
 2. Themethod of claim 1 wherein forming said estimate comprises obtaining:${\hat{z}(n)} = \frac{{{\hat{H}}^{*}(n)}{R_{w}^{- 1}(n)}{X(n)}}{{{\hat{H}}^{*}(n)}{R_{w}^{- 1}(n)}{\hat{H}(n)}}$

wherein n is a frequency domain index for said symbol, Ĥ is an M_(R)length vector estimate of a response of said channel where M_(R) is anumber of said multiple outputs, R_(W) is an M_(R)×M_(R) matrixindicating spatial covariance of noise and/or interference, and X is anM_(R) length vector of received symbols.
 3. The method of claim 2wherein obtaining said channel confidence level comprises obtaining:p(n)=Ĥ*(n)R _(w) ⁻¹(n)Ĥ(n).
 4. The method of claim 1 further comprising:using said cost metric values in a trellis decoding process.
 5. Themethod of claim 4 wherein said cost metric values are obtained for eachbit of said OFDM frequency domain symbol.
 6. The method of claim 1wherein said statistical characterization assumes that said interferenceis spatially uncorrelated among said channel outputs.
 7. The method ofclaim 6 wherein forming said estimate comprises obtaining:${{\hat{z}(n)} = \frac{\sum\limits_{i = 1}^{M_{R}}{{{{\hat{h}}_{i}^{*}(n)}/{\sigma_{i}^{2}(n)}}{x_{i}(n)}}}{\sum\limits_{i = 1}^{M_{R}}{{{{\hat{h}}_{i}(n)}}^{2}/{\sigma_{i}^{2}(n)}}}};\quad {and}$

wherein obtaining said channel confidence level comprises obtaining:${{p(n)} = {\sum\limits_{i = 1}^{M_{R}}{{{\hat{h}(n)}}^{2}/{\sigma_{i}^{2}(n)}}}};\quad \text{and wherein}$

n is a frequency domain index for said symbol, i identifies a particularone of M_(R) channel outputs, ĥ_(i) is the channel response measured atoutput i, x_(i) is the frequency domain symbol received at channeloutput i, and σ_(i) ² is a signal variance as received via a channeloutput i.
 8. The method of claim 1 wherein said statisticalcharacterization assumes that said interference is spatiallyuncorrelated and identically distributed among said channel outputs. 9.The method of claim 8 wherein forming said estimate comprises obtaining:${{\hat{z}(n)} = \frac{\sum\limits_{i = 1}^{M_{R}}{{{\hat{h}}_{i}^{*}(n)}{x_{i}(n)}}}{\sum\limits_{i = 1}^{M_{R}}{{{\hat{h}}_{i}(n)}}^{2}}};\quad {and}$

wherein obtaining said channel confidence level comprises obtaining:${{p(n)} = {\frac{1}{\sigma^{2}(n)}{\sum\limits_{i = 1}^{M_{R}}{{{\hat{h}}_{i}(n)}}^{2}}}};$

and wherein n is a frequency domain index for said symbol, i identifiesa particular one of M_(R) channel outputs, ĥ_(i) is the channel responsemeasured at output i, x_(i) is the frequency domain symbol received atchannel output i, and σ² is an overall signal variance.
 10. The methodof claim 1 wherein said statistical characterization is obtained basedon noise plus interference estimates obtained independently for each ofsaid channel outputs.
 11. The method of claim 10 wherein saidstatistical characterization is obtained by: obtaining an initialestimate of said frequency domain symbol to be:${{\hat{z}}_{i}(n)} = \frac{{{\hat{h}}_{i}^{*}(n)}{x_{i}(n)}}{{{\hat{h}}_{i}^{*}(n)}{{\hat{h}}_{i}(n)}}$

obtaining a noise and interference estimate for each channel output iand for said frequency index n to be: ŵ _(i)(n)=x_(i)(n)−ĥ_(i)(n){circumflex over (z)} _(i)(n);  and obtaining an ijthentry of a covariance matrix R_(W)(n) to be E[ŵ_(i)(n)ŵ*_(j)(n)]; andwherein n is a frequency domain index, i and j identify particularchannel outputs, ĥ_(i)(n) is a channel response estimate at frequencydomain index n based on signal received via channel output i, x_(i)(n)represents a received frequency domain symbol at frequency domain indexn received via channel output i.
 12. The method of claim 1 wherein saidmultiple channel outputs comprise multiple antennas.
 13. In a digitalcommunication system, apparatus for receiving an OFDM signal viamultiple outputs of a channel in the presence of noise and/orinterference, the apparatus comprising: an estimation block that forms astatistical characterization of noise and/or interference received viasaid plurality of channel outputs; a cost metric value processing blockthat forms an estimate of a received OFDM frequency domain symbol basedon said statistical characterization; obtains a channel confidence levelfor a frequency subchannel of said OFDM frequency domain symbol; andobtains a cost metric value of said received OFDM frequency domainsymbol based on said estimate and said channel confidence level.
 14. Theapparatus of claim 13 wherein said cost metric value block forms saidestimate by obtaining:${\hat{z}(n)} = \frac{{{\hat{H}}^{*}(n)}{R_{w}^{- 1}(n)}{X(n)}}{{{\hat{H}}^{*}(n)}{R_{w}^{- 1}(n)}{\hat{H}(n)}}$

wherein n is a frequency domain index for said symbol, Ĥ is an M_(R)length vector estimate of a response of said channel where M_(R) is anumber of said multiple outputs, R_(W) is an M_(R)×M_(R) matrixindicating spatial covariance of noise and/or interference, and X is anM_(R) length vector of received symbols.
 15. The apparatus of claim 14wherein said cost metric value processing block obtains said channelconfidence level by obtaining: p(n)=Ĥ*(n)R _(w) ⁻¹(n)Ĥ(n).
 16. Theapparatus of claim 13 further comprising: a trellis decoder thatreceives said cost metric values and employs said cost metric values indecoding a series of symbols.
 17. The apparatus of claim 16 wherein saidcost metric value processing block obtains said cost metric values foreach bit of said OFDM frequency domain symbol.
 18. The apparatus ofclaim 13 wherein said statistical characterization assumes that saidinterference is spatially uncorrelated among said channel outputs. 19.The apparatus of claim 18 wherein said cost metric value processingblock estimates said frequency domain symbol by obtaining:${{\hat{z}(n)} = \frac{\sum\limits_{i = 1}^{M_{R}}{{{{\hat{h}}_{i}^{*}(n)}/{\sigma_{i}^{2}(n)}}{x_{i}(n)}}}{\sum\limits_{i = 1}^{M_{R}}{{{{\hat{h}}_{i}(n)}}^{2}/{\sigma_{i}^{2}(n)}}}};\quad {and}$

wherein said cost metric value processing block obtains said channelconfidence level by obtaining:${{p(n)} = {\sum\limits_{i = 1}^{M_{R}}{{{\hat{h}(n)}}^{2}/{\sigma_{i}^{2}(n)}}}};$

and wherein n is a frequency domain index for said symbol, i identifiesa particular one of M_(R) channel outputs, ĥ_(i) is the channel responsemeasured at output i, x_(i) is the frequency domain symbol received atchannel output i, and σ_(i) ² is a signal variance as received via achannel output i.
 20. The apparatus of claim 13 wherein said statisticalcharacterization assumes that said interference is spatiallyuncorrelated and identically distributed among said channel outputs. 21.The apparatus of claim 20 wherein cost metric value processing blockestimates said frequency domain symbol by obtaining:${{\hat{z}(n)} = \frac{\sum\limits_{i = 1}^{M_{R}}{{{\hat{h}}_{i}^{*}(n)}{x_{i}(n)}}}{\sum\limits_{i = 1}^{M_{R}}{{{\hat{h}}_{i}(n)}}^{2}}};\quad {and}$

wherein said cost metric value processing block obtains said channelconfidence level by obtaining:${{p(n)} = {\frac{1}{\sigma^{2}(n)}{\sum\limits_{i = 1}^{M_{R}}{{{{\hat{h}}_{i}(n)}}^{2}.}}}};$

and wherein n is a frequency domain index for said symbol, i identifiesa particular one of M_(R) channel outputs, ĥ_(i) is the channel responsemeasured at output i, x_(i) is the frequency domain symbol received atchannel output i, and σ² is an overall signal variance.
 22. Theapparatus of claim 13 wherein said estimation block obtains saidstatistical characterization based on noise plus interference estimatesobtained independently for each of said channel outputs.
 23. Theapparatus of claim 22 wherein said estimation block obtains saidstatistical characterization by: obtaining an initial estimate of saidfrequency domain symbol to be:${{{\hat{z}}_{i}(n)} = \frac{{{\hat{h}}_{i}^{*}(n)}{x_{i}(n)}}{{{\hat{h}}_{i}^{*}(n)}{{\hat{h}}_{i}(n)}}};$

obtaining a noise and interference estimate for each channel output iand for said frequency index n to be: ŵ _(i)(n)=x _(i)(n)−ĥ_(i)(n){circumflex over (z)} _(i)(n);  and obtaining an i, jth entry ofa covariance matrix R_(W)(n) to be E[ŵ_(i)(n)ŵ*_(j)(n)]; and wherein nis a frequency domain index, i and j identify particular channeloutputs, ĥ_(i) (n) is a channel response estimate at frequency domainindex n based on signal received via channel output i, x_(i)(n)represents a received frequency domain symbol at frequency domain indexn received via channel output i.
 24. In a digital communications system,a receiver system comprising: a plurality of connections to outputs of achannel; for each of said channel outputs, an FFT processor thatconverts a time domain symbol stream to a series of frequency domainsymbols; an initial estimator that obtains an initial estimate of afrequency domain symbol to be:${{z_{i}(n)} = \frac{{{\hat{h}}_{i}^{*}(n)}{x_{i}(n)}}{{{\hat{h}}_{i}^{*}(n)}{{\hat{h}}_{i}(n)}}};\quad {and}$

a noise and/or interference estimator that obtains a noise andinterference estimate to be:  ŵ _(i)(n)=x _(i)(n)−ĥ _(i)(n){circumflexover (z)} _(i)(n); and a covariance matrix estimator that obtains an i,jth entry of a covariance matrix R_(W)(n) to be E[ŵ_(i)(n)ŵ*_(j)(n)];and wherein n is a frequency domain index, i identifies a particularchannel output, ĥ_(i)(n) is a channel response estimate at frequencydomain index n based on signal received via channel output i, andx_(i)(n) represents a received frequency domain symbol at frequencydomain index n received via antenna index I.